Sentences with phrase «by sqrt»

Multiplying the standard deviation by sqrt (5) provides a crude adjustment for the autocorrelation, bringing the standard deviation to 0.068 W / m ².

So we can not assume the error will decrease by the sqrt (number of months it is applied).

Aerosol cooling from volcanoes becomes irrelevant, etc. (ii) But even if I had based it on the point that averaging n times as many samples reduces the expected error by sqrt (n), what is «erroneous» about that?

Pardon my ignorance, but we're now halfway through a doubling of CO2 since preindustrial times (the current 392 ppm divided by sqrt (2) is 277 ppm, right in the 260 - 280 ppm range given by Wikipedia for the level just before the industrial emissions began).

Not exact matches

Because heavy - flavor production is dominated by gluon - gluon interactions at $ \ sqrt -LCB- s -RCB- = 200 $ GeV, these measurements offer a unique opportunity... ▽ More The cross section and transverse single - spin asymmetries of $ \ mu ^ -LCB-- -RCB- $ and $ \ mu ^ -LCB- + -RCB- $ from open heavy - flavor decays in polarized $ p $ + $ p $ collisions at $ \ sqrt -LCB- s -RCB- = 200 $ GeV were measured by the PHENIX experiment during 2012 at the Relativistic Heavy Ion Collider.

Because heavy - flavor production is dominated by gluon - gluon interactions at $ \ sqrt -LCB- s -RCB- = 200 $ GeV, these measurements offer a unique opportunity to obtain information on the trigluon correlation functions.

In the simplest case, if the mean increases as N then the standard deviation increases by N / sqrt (N) = sqrt (N).

Steve: I have already posted on Mann's sqrt cos thing, but I'll do a post on censorship by blogs with Climate in their name.

Increasing the number of measurements in this case, does decrease the random component of the instrumental error by 1 / SQRT (n) where n is the the number of observations.

Let's illustrate by an example: I have a system whose dynamics is given by a continuous solution F (t) = Sqrt (at).

When the inter-methodological (+ / --RRB- 2 C noted by Bemis, et al., is added as the rms to the average (+ / --RRB- 1.25 C measurement error from the work of McCrae 1950 and Bemis 1998, the combined 1 - sigma error in determined T = (+ / --RRB- sqrt (1.25 ^ 2 +2 ^ 2) = (+ / --RRB- 2.4 C.

That is, he claimed that the 11 - year sunspot cycle plus its secular and millennial variation, which I was modeling very precisely with my model, could be produced also by this kind of formula f (t) = A * cos (2p * (t - T1) / p1) + B * cos (2p * (t - T2) / p2) Some variation on that formula does a good job, e.g. the one I used in my toy - example: «Sunspot Number» = SQRT (ABS (k * cos (π / p1 * t) + cos (π / p2 * t)-RRB--RRB-

That simplifies the discussion as then we can estimate \ (2 \ sigma \ approx 2 -LCB- \ sqrt -LCB- V / (N - 1)-RCB--RCB- \), where N is given by the number of uncorrelated Atlantic ocean areas between 20 ° N and 20 ° S. With a correlation length of ∼ 10 — 15 ° we obtain a rough estimate of N ≈ 12 for the tropical Atlantic sector.

If each point in the right slide is obtained as the average of 100 more or less normally distributed points in the left slide, the errors bars shrink by a factor of sqrt (100) = 10.

res.mean = by (yamal, list (yamal $ age, yamal $ life), function (x) mean (x $ resids)-RRB- res.scmean = by (yamal, list (yamal $ age, yamal $ life), function (x) sqrt (nrow (x)-RRB- * (mean (x $ resids)-1) / sd (x $ resids)-RRB-